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Econometric Analysis is intended for a one-year graduate course in econometrics for social scientists. The prerequisites for this course should include calculus, mathematical statistics, and an introduction to econometrics at the level of, say, Gujarati’s Basic Econometrics(McGraw-Hill, 1995) or Wooldridge’s Introductory Econometrics: A Modern Approach [South-Western (2000)]. Self-contained (for our purposes) summaries of the matrix algebra, mathematical statistics, and statistical theory used later in the book are given in Appendices A through D. Appendix E contains a description of numerical methods that will be useful to practicing econometricians. The formal presentation of econometrics begins with discussion of a fundamental pillar, the linear multiple regression model, in Chapters 2 through 8. Chapters 9 through 15 present familiar extensions of the single linear equation model, including nonlinear regression, panel data models, the generalized regression model, and systems of equations. The linear model is usually not the sole technique used in most of the contemporary literature. In view of this, the (expanding) second half of this book is devoted to topics that will extend the linear regression model in many directions. Chapters 16 through 18 present the techniques and underlying theory of estimation in econometrics, including GMM and máximum likelihood estimation methods and simulation based techniques.We end in the last four chapters, 19 through 22, with discussions of current topics in applied econometrics, including time-series analysis and the analysis of discrete choice and limited dependent variable models.